Making things clear: Jonathan Kozol writes, "'The education industry', according to these analysts, 'represents, in our opinion, the final frontier of a number of sectors once under public control' that 'have either voluntarily opened' or, they note in pointed terms, have 'been forced' to open up to private enterprise." Or, James Calantjis adds: "Five Educational Myths (used to fuel ed deform):
1) It all happens in the classroom.
2) The teacher is the most important factor in learning.
3) More money will improve education.
4) Smaller Learning Communities (including smaller schools) will improve education.
5) All children must be prepared for college."

Sometimes a programming language can be a thing of beauty. CoffeeScript is a case in point, taking ugly ugly Javascript (or the even more ugly-named EMCA script) and turning it into something beautiful - and point for point compatible. It is what Javascript would have looked like had some thought gone into readability and design in the first place. "Underneath all of those embarrassing braces and semicolons, JavaScript has always had a gorgeous object model at its heart. CoffeeScript is an attempt to expose the good parts of JavaScript in a simple way."

The concept of activity streams, as described here by George Siemens, is a fairly natural progression from the idea of the personal learning environment, and the diagram (above) makes that clear, as we have the same usual suspects - blogs, photo sites, chats, etc. - feeding into a personal system. The major different is in how the system is portrayed, and (if I may wax sceptically for the moment) and this portrayal is firmly in step with the now vogue notions of data and analytics. Not that there's nothing to these, but wrapping up the old idea in the new terminology isn't a great advance. So, if we are in some way going to learn from activity streams, we want a story of what we will learn and how the activity streams help us do this. What we get here is, "splicing information and social interactions is critical to making sense of activity streams... the need for mechanisms to order that information and flow soon becomes evident." We need more.

One of the things I have been uncertain about regarding the massive online courses (MOOCs) George Siemens and I have been offering is the fact that they are courses, with the start-stop staccato network formation courses imply. But after three years of experience doing these, I'm seeing it a bit differently - that throwing out courses as we do is disruptive, that it shakes up the existing network, breaks up in-groups as ossifying structures, and gives newcomers a chance to start as equals. Courses are, essentially, a Boltzmann mechanism for learning networks (at least, when they are offered as open online courses). We see this diagrammed here: this forum diagram from CCK09 shows the integration of new participants deep into the network of more experienced participants, rather than the two separate subgroups we might expect without the disruptive influence of a course closing and a new one starting up. There's another such diagram here, and more here, showing this isn't an isolated phenomenon.

Good account of the design research methodology and "personal experience gained by participating in design processes of four distinct and experimental software learning tools. These learning tools aimed to enhance learning in different socio-technical contexts." Teemu Leinonen is a lot more structured in his research that I am in mine - I do all the same things (and probably a few more irrelevant things), but in no particular order and without a research-specific intent. I am, as Feyerabend would say, against method, because I believe that method predetermines results. But I don't recommend my approach for everyone.

Networks within networks. As described by a NY Times article here, and in a Sanger Institute press release here, each of the brain's 100 trillion synapses, which are connections joining neurons together, are themselves networks composed of some 1,461 separate proteins. "From the synapses, the proteins were identified and as shown in the image, each protein can be represented as a point, and the lines show the connections between proteins. This shows how the many proteins in the PSD are connected in a network or roadmap. Many of these proteins are involved in human diseases and these are shown as 'stars' in the protein network map." The scientific publication, 'Characterization of the proteome, diseases and evolution of the human postsynaptic density,' was published in Nature on the 19th, and has not yet appeared in PubMed. Data from the research is freely available online here. Via @Cris2B.

As the web of data approaches, we have ever newer methods for identifying celebrities and stars. A case in point is Traackr, surveyed here by Bill Ives, which uses a variety of keyword-based metrics to rank authors in scales it calls 'reach', 'resonance' and 'relevance', a triumvirate of quality used to create A-List rankings in various disciplines. Ives has set up an A-List of people in enterprise social media. "People dominate brands," explains Traackr as it describes its approach. "These online influencers mastered the art of capturing the attention of an audience and sparking conversations on the web around issues, products and brands they care about." There's a distinction worth drawing here, that the people who capture Traacker's attention are not the experts in the field, but rather those in the field who are experts at capturing attention.

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